Conventional approaches to indicators and metrics undermine urban climate adaptation
Bibliographic record
Abstract
Measurement is essential for effective adaptation management and operation, and indicators and metrics (I&M) have a pivotal role. Surprisingly, systematic efforts to assess advances in the provision of adaptation I&M are scarce, and those that do exist often lack in-depth analysis of the types, characteristics, and applicability of the collected information. Here, we analyse 137 publications and 901 I&M sourced in the scientific literature (2007-2022) to measure adaptation to climate change in urban areas where governments are increasingly placing efforts to prepare populations and infrastructures. A lack of common terminology, standardisation, and guidelines has resulted in a field that is complex to track and understand. This complexity has led to a fragmented methodological landscape, marked by diverse, context-dependent, and occasionally conflicting approaches to the development of I&M. We argue that conventional approaches to I&M are largely inadequate and must better emphasise quantifiability, long-term assessment, and alignment with policy objectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.053 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".